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Convolutional neural networks for heat conduction
Case Studies in Thermal Engineering, 2022 This paper presents a data-driven approach to solve heat conduction problems, in particular 2D heat conduction problems. The physical laws which govern such problems are modeled by partial differential equations.
Sidharth Tadeparti +1 more
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Building Program Vector Representations for Deep Learning [PDF]
, 2014 Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc.
Jin, Zhi +6 more
core +1 more source
IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons.
Enrique S. Marquez +2 more
openaire +5 more sources
Applied Sciences, 2021 To properly restore masonry cultural heritage sites, the materials used for retrofitting can have a critical effect, and this requires standards for traditional Korean brick and lime mortar to be examined.
Gayoon Lee +4 more
doaj +1 more source
Approximations in Deep Learning
, 2022 Approximate Computing Techniques - From Component- to Application-Level, pp.467-512, 2022, 978-3-030-94704 ...
Dupuis, Etienne +5 more
openaire +3 more sources
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction [PDF]
, 2017 Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision
Kumar, Devinder +2 more
core +3 more sources
ESANN 2021 proceedings, 2021 Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators.
Bacciu, Davide +3 more
openaire +3 more sources
A deep-learning approach for high-speed Fourier ptychographic microscopy [PDF]
, 2018 We demonstrate a new convolutional neural network architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM.https://www.researchgate.net ...
Li, Yunzhe +5 more
core +1 more source
IEEE Access, 2020 A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing ...
Cheol Young Park +3 more
doaj +1 more source
, 2020 Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (
Carver, Eric +11 more
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